The clinical course of chronic illnesses such as diabetes mellitus type 2 (i.e., “type 2 DM”) is generally characterized by slowly changing states of health, with inherent variability in the rate of disease progression across patients. The detection of significant change in health status of individual patients may be masked by the subtle progression of the disease and its complications, and of common comorbid conditions. Physicians often fail to prescribe appropriate evidence-based treatment in patients who have not achieved recommended clinical goals for a variety of reasons. There are several confounding variables that affect patients' response to treatment. For example, changes in socioeconomic status or health insurance coverage may alter patterns of care or medication adherence.
The cognitive challenges to physicians caring for those with a complex chronic disease such as diabetes are many. Some of the major cognitive tasks include: selecting appropriate evidence-based clinical goals across multiple clinical domains (for example, glucose, blood pressure, cholesterol); initiating appropriate therapy; titrating therapy to achieve and maintain desired evidence-based goals; and detecting and effectively managing comorbid conditions, such as depression, that may interfere with diabetes treatment.
Computer-based models and simulation methods have been used to better understand and improve diabetes care. Diabetes Physiolab® is a proprietary system that models disease at the level of enzymatic activity. Other efforts have modeled general diabetes physiology, pharmacokinetics, specific glucose-insulin interactions as educational simulators, as well as diabetes decision-support systems. The Global Diabetes Model, a stochastic model of type 2 DM, has been developed to predict trends for diabetic individuals or populations. A recent model—Archimedes—has simulated a continuous disease process at the individual patient level. Other case-based learning efforts exist that are used for physician continuing education.
However, existing models do not provide dynamic feedback or learning where the long-term effect of previous physician moves can be represented. They also do not provide a focus on the clinical physician-patient encounter, which is the basis for the study and improvement of physician decision-making.